1. Field of the Invention
The present invention generally relates to a receiving apparatus and method of a broadband wireless access system, and more particularly, to a hybrid channel estimating apparatus which considers both the complexity and performance of Orthogonal Frequency Division Multiplexing (OFDM)-based system, and a method thereof.
2. Description of the Related Art
Among many wireless communication schemes proposed for high-speed mobile communications, OFDM is considered to be the most prominent candidate for the next-generation wireless communication technology. OFDM is expected to be used in most of the 4-generation wireless communications, and has been selected as the standards for the Wireless Metropolitan Area Network, 802.16, which is so-called 3.5-G technology.
An OFDM-based broadband wireless access system provides improved performance by use of coherent detection of accurate channel estimation. Accuracy in channel estimation is a very important factor, especially in order to incorporate an advanced mobile communication scheme using an instantaneous channel characteristic which is currently under active research. Channel estimation, as used herein, refers to estimating channel impulse response, and an OFDM system estimates channel impulse response by use of filtering of pilots arranged in 2-dimensional time and frequency domains.
A 2-dimension Wiener channel estimation filtering is currently the most optimum pilot-based channel estimation scheme. Wiener channel estimation filtering minimizes Minimum Mean Square Error (MMSE), and provides optimum channel estimation, by appropriately adjusting a tap coefficient according to a channel condition. However, Wiener channel estimation filtering has a drawback of high complexity of computation, because computations, such as matrix inverse operation, to obtain the most suitable filter tap coefficient, and complicated multiplications during the filtering process, are necessary, and this subsequently causes high hardware complexity. A 2×1 dimension Wiener channel estimation filtering has been proposed to overcome the above drawback. The 2×1 dimension Wiener channel estimation filtering uses two joint 1-dimension Wiener channel estimation filters to provide low complexity as well as the similar channel estimation performance as the Wiener channel estimation filtering. However, computational complexity increases as the number of Wiener channel estimation filters, or filter taps increases.
In order to overcome the problems mentioned above, relatively simpler interpolation filtering, such as Lagrange or Spline interpolation, can be used. Unlike the Wiener filtering, which adaptively changes a filter parameter according to a channel condition, schemes such as Lagrange or Spline interpolation use fixed filter parameter, irrespective of the channel condition. Therefore, complexity can be minimized. However, severe performance degradation is experienced under certain channel environments.
As explained above, a Wiener channel estimation filter operates adaptively according to channel status and thus provides optimum performance. However, it is difficult to fully utilize this scheme due to high complexity. Interpolation filters are relatively simpler to construct, but these do not ensure appropriate level of channel estimation performance. Accordingly, a channel estimation scheme, which can provide a tradeoff between complexity and channel estimation quality, is necessary.